The <i>British Journal of Music Therapy</i> : A 25-year retrospective
Bibliographic record
Abstract
To mark 25 years of the British Journal of Music Therapy (BJMT) since the millennium in 2000, we have invited editors of the journal over the past quarter century to reflect on their time in this role and offer their thoughts about the music therapy profession. Their responses are presented here with minimal editing and without commentary as a contribution to the history of BJMT and its role in the music therapy profession. Exceptionally for BJMT, this article has not been peer- reviewed, but all authors have read each other’s contributions and offered corrections of fact where needed. We are grateful to them for the time and effort they have put into responding to this initiative. Each contributor was asked to reflect on the issues they faced during their time as editor, and to choose articles published under their editorship which they felt represented significant developments in practice or thinking in the profession. They were also invited to give their thoughts on the current and future role for BJMT. Articles are not referenced in the standard way but titles, authors, year of publication and BJMT issue numbers are given, with active links in the online edition of the journal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".